Holmes, Chris C., Caron, Francois, Griffin, Jim E., Stephens, David A. (2015) Two-sample Bayesian nonparametric hypothesis testing. Bayesian Analysis, 10 . pp. 297-320. ISSN 1936-0975. (doi:10.1214/14-BA914) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:50193)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
| Official URL: http://dx.doi.org/10.1214/14-BA914 |
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| Additional URLs: |
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Abstract
In this article we describe Bayesian nonparametric procedures for two-sample hypothesis testing. Namely, given two sets of samples y(1)˜F(1) and y(2)˜F(2), with F(1),F(2) unknown, we wish to evaluate the evidence for the null hypothesis H0:F(1)?F(2) versus the alternative H1:F(1)?F(2). Our method is based upon a nonparametric Pólya tree prior centered either subjectively or using an empirical procedure. We show that the Pólya tree prior leads to an analytic expression for the marginal likelihood under the two hypotheses and hence an explicit measure of the probability of the null Pr(H0|{y(1),y(2)})
| Item Type: | Article |
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| DOI/Identification number: | 10.1214/14-BA914 |
| Uncontrolled keywords: | Bayesian nonparametrics, Polya tree, hypothesis testing |
| Subjects: | Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
| Institutional Unit: | Schools > School of Engineering, Mathematics and Physics > Mathematical Sciences |
| Former Institutional Unit: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
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| Depositing User: | Jim Griffin |
| Date Deposited: | 14 Aug 2015 13:16 UTC |
| Last Modified: | 20 May 2025 11:37 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/50193 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Griffin, Jim E..
| Creator's ORCID: |
 https://orcid.org/0000-0002-4828-7368
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